Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice.

OBJECTIVE: To identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated. DESIGN: A mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts. SETTING: Three urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics. PARTICIPANTS: Observation and interviews with 65 primary care staff in the following job roles: administrator, manager, general practitioner, healthcare assistant, nurse practitioner, pharmacy technician, phlebotomist, practice nurse, pharmacist, prescription clerk, receptionist, scanning clerk, secretary and medical summariser; together with a survey of 156 experts in automation technologies. METHODS: 330 hours of ethnographic observation and documentation of administrative tasks carried out by staff in each of the above job roles, followed by coding and classification; semistructured interviews with 10 general practitioners and 6 staff focus groups. The online survey of machine learning, artificial intelligence and robotics experts was analysed using an ordinal Gaussian process prediction model to estimate the automatability of the observed tasks. RESULTS: The model predicted that roughly 44% of administrative tasks carried out by staff in general practice are 'mostly' or 'completely' automatable using currently available technology. Discussions with practice staff underlined the need for a cautious approach to implementation. CONCLUSIONS: There is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.

The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System.

BACKGROUND: Recent advances in technology have reopened an old debate on which sectors will be most affected by automation. This debate is ill served by the current lack of detailed data on the exact capabilities of new machines and how they are influencing work. Although recent debates about the future of jobs have focused on whether they are at risk of automation, our research focuses on a more fine-grained and transparent method to model task automation and specifically focus on the domain of primary health care. OBJECTIVE: This protocol describes a new wave of intelligent automation, focusing on the specific pressures faced by primary care within the National Health Service (NHS) in England. These pressures include staff shortages, increased service demand, and reduced budgets. A critical part of the problem we propose to address is a formal framework for measuring automation, which is lacking in the literature. The health care domain offers a further challenge in measuring automation because of a general lack of detailed, health care-specific occupation and task observational data to provide good insights on this misunderstood topic. METHODS: This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic inference of task- and occupation-level automation to gain valuable insights. Sensitivity analysis is then used to present the occupational attributes that increase/decrease automatability most, which is vital for establishing effective training and staffing policy. RESULTS: Our detailed fieldwork includes observing and documenting 16 unique occupations and performing over 130 tasks across six primary care centers. Preliminary results on the current state of automation and the potential for further automation in primary care are discussed. Our initial findings are that tasks are often shared amongst staff and can include convoluted workflows that often vary between practices. The single most used technology in primary health care is the desktop computer. In addition, we have conducted a large-scale survey of over 156 machine learning and robotics experts to assess what tasks are susceptible to automation, given the state-of-the-art technology available today. Further results and detailed analysis will be published toward the end of the project in early 2019. CONCLUSIONS: We believe our analysis will identify many tasks currently performed manually within primary care that can be automated using currently available technology. Given the proper implementation of such automating technologies, we expect considerable staff resources to be saved, alleviating some pressures on the NHS primary care staff. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11232.

Kalata B1 and Kalata B2 Have a Surfactant-Like Activity in Phosphatidylethanolomine-Containing Lipid Membranes.

Cyclotides are cyclic disulfide-rich peptides that are chemically and thermally stable and possess pharmaceutical and insecticidal properties. The activities reported for cyclotides correlate with their ability to target phosphatidylethanolamine (PE)-phospholipids and disrupt cell membranes. However, the mechanism by which this disruption occurs remains unclear. In the current study we examine the effect of the prototypic cyclotides, kalata B1 (kB1) and kalata B2 (kB2), on tethered lipid bilayer membranes (tBLMs) using swept frequency electrical impedance spectroscopy. We confirmed that kB1 and kB2 bind to bilayers only if they contain PE-phospholipids. We hypothesize that the increase in membrane conduction and capacitance observed upon addition of kB1 or kB2 is unlikely to result from ion channel like pores but is consistent with the formation of lipidic toroidal pores. This hypothesis is supported by the concentration dependence of effects of kB1 and kB2 being suggestive of a critical micelle concentration event rather than a progressive increase in conduction arising from increased channel insertion. Additionally, conduction behavior is readily reversible when the peptide is rinsed from the bilayer. Our results support a mechanism by which kB1 and kB2 bind to and disrupt PE-containing membranes by decreasing the overall membrane critical packing parameter, as would a surfactant, which then opens or increases the size of existing membrane defects. The cyclotides need not participate directly in the conductive pore but might exert their effect indirectly through altering membrane packing constraints and inducing purely lipidic conductive pores.

Transient potential gradients and impedance measures of tethered bilayer lipid membranes: pore-forming peptide insertion and the effect of electroporation.

In this work, we present experimental data, supported by a quantitative model, on the generation and effect of potential gradients across a tethered bilayer lipid membrane (tBLM) with, to the best of our knowledge, novel architecture. A challenge to generating potential gradients across tBLMs arises from the tethering coordination chemistry requiring an inert metal such as gold, resulting in any externally applied voltage source being capacitively coupled to the tBLM. This in turn causes any potential across the tBLM assembly to decay to zero in milliseconds to seconds, depending on the level of membrane conductance. Transient voltages applied to tBLMs by pulsed or ramped direct-current amperometry can, however, provide current-voltage (I/V) data that may be used to measure the voltage dependency of the membrane conductance. We show that potential gradients >~150 mV induce membrane defects that permit the insertion of pore-forming peptides. Further, we report here the novel (to our knowledge) use of real-time modeling of conventional low-voltage alternating-current impedance spectroscopy to identify whether the conduction arising from the insertion of a polypeptide is uniform or heterogeneous on scales of nanometers to micrometers across the membrane. The utility of this tBLM architecture and these techniques is demonstrated by characterizing the resulting conduction properties of the antimicrobial peptide PGLa.

Square wave voltammetry versus electrochemical impedance spectroscopy as a rapid detection technique at electrochemical immunosensors.

Square wave voltammetry (SWV) was compared to electrochemical impedance spectroscopy (EIS) in developing a label-free electrochemical immunosensor for the hormone estradiol. The immunosensor consists of a Au electrode anchored with a Au nanoparticle|thiolated Protein G-scaffold to facilitate immobilisation of an enhanced quantity of an almost uprightly aligned anti-estradiol capture antibody. Competitive immunoassays between an estradiol-bovine saline albumin complex and free estradiol in a sample were then promoted at the immunosensor. Next, SWV and EIS of [Fe(CN)(6)](3-/4-) were sequentially conducted at the immunosensor. SWV yielded familiar peak-shaped voltammograms with the peak currents readily employable in establishing calibration. A dynamic range up to approximately 1200 pg mL(-1) and a detection limit of 18 pg mL(-1) estradiol were achieved. In EIS, an electron transfer resistance estimated from the Nyquist plots was used in the calibration experiments. A comparable dynamic range up to approximately 1000 pg mL(-1) and a detection limit of 26 pg mL(-1) estradiol were obtained. However, a significantly 10 times longer analysis time and substantial effort were required to complete the EIS determinations relative to SWV. Moreover, a large amount of EIS data involving phase angle was collected but ignored because they would not contribute any useful information to quantitative determination. Overall, SWV was determined to be a more rapid, efficient, effective and low cost detection technique than EIS at label-free electrochemical immunosensors.